Analysis of the subtleties of the voluminous amounts of genetic information will continue to have profound effects on the personalization of medicine. For example, this advanced genetic knowledge of patients has and will continue to have broad impact on the ability to diagnose diseases, identify predispositions to diseases or other genetically impacted disorders, the ability to identify reactivity to given drugs or other treatments, whether adverse or beneficial.
Before one can begin to interpret genetic data from patients, one must first obtain the genetic information from that patient. Technologies have been developed that allow for broad screening of large swaths of a patient's genetic code by identifying key signposts in that code and using this fragmented data as a general interpretation mechanism, e.g., using libraries of known genetic variations, such as SNPs or other polymorphisms, and correlating the profile of such variations against profiles that have a suspected association with a given disease or other phenotype.
Rather than rely upon disparate pieces of information from the genetic code, it would be of far more value to be able to rely upon the entire text of a patient's genetic code in making any interpretations from that code. In using an analogy of a novel, one gains a substantially deeper understanding of all the elements of the novel, e.g., plot, characters, setting etc., by reading the entire text, rather than by picking out individual words from disparate pages or chapters of the novel.
Technologies related to analysis of biological information have advanced rapidly over the past decade. In particular, with the improved ability to characterize genetic sequence information, identify protein structure, elucidate biological pathways, and manipulate any or all of these, has come the need for improved abilities to derive and process this information.
In the field of genetic analysis, for example, faster and faster methods of obtaining nucleic acid sequence information have consequences in terms of requiring different and often times better methods and processes for processing the raw genetic information that is generated by these processes. This progress has been evidenced in the improvements applied to separations based Sanger sequencing, where improvements in throughput and read-length have come not only through multiplexing of multi-capillary systems, but also from improvements in base calling processes that are applied to the data derived from the capillary systems.
With shifts in the underlying technology surrounding genetic analysis, also comes a necessity for a shift in the methods and processes for processing the information from these systems. The present invention provides solutions to these and other problems.
Various embodiments and components of the present invention employ pulse, signal, and data analysis techniques that are familiar in a number of technical fields. For clarity of description, details of known techniques are not provided herein. These techniques are discussed in a number of available references works, such as: R. B. Ash. Real Analysis and Probability. Academic Press, New York, 1972; D. T. Bertsekas and J. N. Tsitsiklis. Introduction to Probability. 2002; K. L. Chung. Markov Chains with Stationary Transition Probabilities, 1967; W. B. Davenport and W. L Root. An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, New York, 1958; S. M. Kay, Fundamentals of Statistical Processing, Vols. 1-2, (Hardcover—1998); Monsoon H. Hayes, Statistical Digital Signal Processing and Modeling, 1996; Introduction to Statistical Signal Processing by R. M. Gray and L. D. Davisson; Modern Spectral Estimation Theory and Application/Book and Disk (Prentice-Hall Signal Processing Series) by Steven M. Kay (Hardcover—January 1988); Modern Spectral Estimation: Theory and Application by Steven M. Kay (Paperback—March 1999); Spectral Analysis and Filter Theory in Applied Geophysics by Burkhard Buttkus (Hardcover—May 11, 2000); Spectral Analysis for Physical Applications by Donald B. Percival and Andrew T. Walden (Paperback—Jun. 25, 1993); Astronomical Image and Data Analysis (Astronomy and Astrophysics Library) by J.-L. Starck and F. Murtagh (Hardcover—Sep. 25, 2006); Spectral Techniques In Proteomics by Daniel S. Sem (Hardcover—Mar. 30, 2007); Exploration and Analysis of DNA Microarray and Protein Array Data (Wiley Series in Probability and Statistics) by Dhammika Amaratunga and Javier Cabrera (Hardcover—Oct. 21, 2003).